Some works focus specifically on the end effector. For example, UniGrasp [1]} and AdaGrasp [2]} train policies which can handle various 2 and 3-fingered grippers including those not seen in training. UniGrasp learns to output contact points (which correspond to fingertip locations) for stable grasps given gripper and object point clouds. AdaGrasp, which takes the FCN action-value map approach, takes advantage of cross-correlation (or “cross convolution” [3]}) using voxel representations of the gripper and scene; our architecture also uses cross convolution but focuses on unifying gripper and tool use.
